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International Conference on Rough Sets and Current Trends in Computing

RSCTC 2010: Rough Sets and Current Trends in Computing pp 446–455Cite as

Layered Approximation Approach to Knowledge Elicitation in Machine Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6086))

Abstract

Domain knowledge elicitation constitutes a crucial task in designing effective machine learning algorithm, and is often indispensable in problem domains that display a high degree of internal complexity such as knowledge discovery and data mining, the recognition of structured objects, human behavior prediction, or multi-agent cooperation. We show how to facilitate this difficult and sometimes tedious task with a hierarchical concept learning scheme, designed to cope with the inherent vagueness and complexity of knowledge therein used. We present how our approach, based on Rough Mereology and Approximate Reasoning frameworks, correlate to other well established approaches to machine learning.

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References

  1. Domingos, P.: Toward knowledge-rich data mining. Data Minning and Knowledge Discovery 15(1), 21–28 (2007)

    Article  MathSciNet  Google Scholar 

  2. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience Publication, Hoboken (2000)

    Google Scholar 

  3. Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer, New York (2003)

    Google Scholar 

  4. Freund, Y., Schapire, R.E.: A short introduction to boosting. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp. 1401–1406. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  5. Kurkova, V.: Learning from data as an inverse problem. In: 16th Symposium on Computational Statistic (COMPSTAT 2004), pp. 1377–1384. Physica-Verlag, Heidelberg (2004)

    Google Scholar 

  6. Laird, J.E., Rosenbloom, P.S., Newell, A.: Chunking in soar: The anatomy of a general learning mechanism. Mach. Learn. 1(1), 11–46

    Google Scholar 

  7. Langley, P., Laird, J.E.: Cognitive architectures: Research issues and challenges, Technical Report (2002)

    Google Scholar 

  8. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  9. Mostow, D.J.: Machine transformation of advice into a heuristic search procedure. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, pp. 367–403. Springer, Heidelberg (1984)

    Google Scholar 

  10. Newell, A.: Unified theories of cognition. Harvard University Press, Cambridge (1994)

    Google Scholar 

  11. Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S.: Layered learning for concept synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)

    Google Scholar 

  12. Nguyen, T.T.: Domain knowledge assimilation by learning complex concepts. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 617–626. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Nguyen, T.T.: Domain knowledge assimilation by learning complex concepts. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 617–626. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  14. Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In: International Conference on Pattern Recognition (ICPR 2002), pp. 568–571 (2002)

    Google Scholar 

  15. Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. Journal of Approximate Reasoning 15(4), 333–365 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  16. Schapire, R.E., Rochery, M., Rahim, M.G., Gupta, N.: Incorporating prior knowledge into boosting. In: ICML 2002: Proceedings of the Nineteenth International Conference on Machine Learning, pp. 538–545. Morgan Kaufmann Publishers Inc., San Francisco (2002)

    Google Scholar 

  17. Shavlik, J.W., Towell, G.G.: An approach to combining explanation-based and neural learning algorithms. Connection Science 1, 231–254 (1989)

    Article  Google Scholar 

  18. Skowron, A.: Rough sets in perception-based computing. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 21–29. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  19. Skowron, A., Polkowski, L.: Rough mereological foundations for design, analysis, synthesis, and control in distributed systems. Information Sciences 104(1-2), 129–156 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  20. Vapnik, V.N.: The Nature of Statistical Learning Theory (Information Science and Statistics). Springer, Heidelberg (1999)

    Google Scholar 

  21. Zadeh, L.A.: From imprecise to granular probabilities. Fuzzy Sets and Systems 154(3), 370–374 (2005)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Tuan Trung Nguyen

Authors
  1. Tuan Trung Nguyen
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Editor information

Editors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Marcin Szczuka

  2. ICS, Warsaw University of Technology,,  

    Marzena Kryszkiewicz

  3. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  4. Dept. of Computer Science, The University of Wales, Aberystwyth, UK

    Richard Jensen

  5. Harbin Institute of Technology, PO Box 458, 150006, Harbin, China

    Qinghua Hu

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Nguyen, T.T. (2010). Layered Approximation Approach to Knowledge Elicitation in Machine Learning. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_48

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  • DOI: https://doi.org/10.1007/978-3-642-13529-3_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13528-6

  • Online ISBN: 978-3-642-13529-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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